/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 *    EntropySplitCrit.java
 *    Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.trees.j48;

import weka.core.ContingencyTables;
import weka.core.Utils;

/**
 * Class for computing the entropy for a given distribution.
 *
 * @author Eibe Frank (eibe@cs.waikato.ac.nz)
 * @version $Revision$
 */
public final class EntropySplitCrit extends EntropyBasedSplitCrit {

    /** for serialization */
    private static final long serialVersionUID = 5986252682266803935L;

    /**
     * Computes entropy for given distribution.
     */
    public final double splitCritValue(Distribution bags) {

        return newEnt(bags);
    }

    /**
     * Computes entropy of test distribution with respect to training distribution.
     */
    public final double splitCritValue(Distribution train, Distribution test) {

        double result = 0;
        int numClasses = 0;
        int i, j;

        // Find out relevant number of classes
        for (j = 0; j < test.numClasses(); j++)
            if (Utils.gr(train.perClass(j), 0) || Utils.gr(test.perClass(j), 0))
                numClasses++;

        // Compute entropy of test data with respect to training data
        for (i = 0; i < test.numBags(); i++)
            if (Utils.gr(test.perBag(i), 0)) {
                for (j = 0; j < test.numClasses(); j++)
                    if (Utils.gr(test.perClassPerBag(i, j), 0))
                        result -= test.perClassPerBag(i, j) * Math.log(train.perClassPerBag(i, j) + 1);
                result += test.perBag(i) * Math.log(train.perBag(i) + numClasses);
            }

        return result / ContingencyTables.log2;
    }

}
